Controller Area Network (CAN) is the de facto standard for in-vehicle networks. However, it is inherently vulnerable to various attacks due to the lack of security features. Intrusion detection systems (IDSs) are considered effective approaches to protect in-vehicle networks. IDSs based on advanced deep learning algorithms have been proposed to achieve higher detection accuracy. However, those systems generally involve high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory and energy costs, we propose a new IDS system using Binarized Neural Network (BNN). Compared to full-precision counterparts, BNNs can offer faster detection, smaller memory cost, and lower energy consumption. Moreover, BNNs can be further accelerated by leveraging Field-Programmable Grid Arrays (FPGAs) since BNNs cut down the hardware consumption. The proposed IDS is based on a BNN model that suits CAN traffic messages and takes advantage of sequential features of messages rather than each individual message. We also explore various design choices of BNN, including increasing network width and depth, to improve accuracy as BNNs typically sacrifice accuracy. The performance of our IDS is evaluated with four different real vehicle datasets. Experimental results show that the proposed IDS reduces the detection latency (3 times faster) on the same CPU platform while maintaining acceptable detection rates compared with full-precision models. We also examine the proposed IDS on multiple platforms, and our results show that using FPGA hardware reduces the detection latency dramatically (128 times faster) with lower power consumption compared to an embedded CPU device. Furthermore, we evaluate BNNs with different designs. Our experimental results demonstrate that wider or deeper models definitely improve accuracy at the cost of increased latency and model sizes to varying degrees. Applications are recommended to choose the appropriate model design they need depending on available resources they have.

<div class="section abstract"><div class="htmlview paragraph">Controller Area Network (CAN), the de facto standard for in-vehicle networks, has insufficient security features and thus is inherently vulnerable to various attacks. To protect CAN bus from attacks, intrusion detection systems (IDSs) based on advanced deep learning methods, such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), have been proposed to detect intrusions. However, those models generally introduce high latency, require considerable memory space, and often result in high energy consumption. To accelerate intrusion detection and also reduce memory requests, we exploit the use of Binarized Neural Network (BNN) and hardware-based acceleration for intrusion detection in in-vehicle networks. As BNN uses binary values for activations and weights rather than full precision values, it usually results in faster computation, smaller memory cost, and lower energy consumption than full precision models. Moreover, unlike other deep learning methods, BNN can be further accelerated by leveraging Field-Programmable Grid Arrays (FPGAs) since BNN cuts down the hardware consumption. We design our BNN model to suit CAN traffic data and exploit sequential features of the CAN traffic instead of individual messages. We evaluate the proposed IDS with four different real vehicle datasets. Our experimental results show that the proposed BNN-based IDS reduces the detection latency on the same CPU (3 times faster) while maintaining acceptable detection rates compared to full precision models. We also implement the proposed IDS using FPGA hardware to reduce latency further and accelerate intrusion detection. Our experiments on multiple platforms demonstrate that using the FPGAs dramatically reduces the detection latency (128 times faster) with lower power consumption in comparison with an embedded CPU.</div></div>

Mono-allelic loss-of-function variants in ARFGEF1 have recently caused a developmental delay, intellectual disability, and epilepsy, with varying clinical expressivity. However, given the clinical heterogeneity and low-penetrance mutations of ARFGEF1-related neurodevelopmental disorder, the robustness of the gene-disease association requires additional evidence. In this study, five novel heterozygous ARFGEF1 variants were identified in five unrelated pediatric patients with neurodevelopmental disorders, including one missense change (c.3539T&gt;G), two canonical splice site variants (c.917-1G&gt;T, c.2850+2T&gt;A), and two frameshift (c.2923_c.2924delCT, c.4951delG) mutations resulting in truncation of ARFGEF1. The pathogenic/likely pathogenic variants presented here will be highly beneficial to patients undergoing genetic testing in the future by providing an expanded reference list of disease-causing variants.

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